Audio Preview Cues: Interaction Aides for Exploration of Online Music and Beyond

We present a light weight mechanism called preview cues that allows non-experts to explore an audio collection by providing supporting information (analogous to the use of tooltips) at the point of interest

Shared Input Multimodal Mobile Interfaces: Interaction Modality Effects on Menu Selection in Single-task and Dual-task Environments

ABSTRACT Audio and visual modalities are two common output channels in the user interfaces embedded in today's mobile devices. However, these user interfaces typically center on the visual modality as the primary output channel, with audio output serving a secondary role. This paper argues for an increased need for shared input multimodal user interfaces for mobile devices. A shared input multimodal interface can be operated independently using a specific output modality, leaving users to choose the preferred method of interaction in different scenarios. We evaluate the value of a shared input multimodal menu system in both a single-task desktop setting and in a dynamic dual-task setting, in which the user was required to interact with the shared input multimodal menu system while driving a simulated vehicle. Results indicate that users were faster at locating a target item in the menu when visual feedback was provided in the single-task desktop setting, but in the dual-task driving setting, visual output presented a significant source of visual distraction that interfered with driving performance. In contrast, auditory output mitigated some of the risk associated with menu selection while driving. A shared input multimodal interface allows users to take advantage of multiple feedback modalities properly, providing a better overall experience

Shared Input Multimodal Mobile Interfaces: Interaction Modality Effects on Menu Selection in Single-task and Dual-task Environments

ABSTRACT Audio and visual modalities are two common output channels in the user interfaces embedded in today's mobile devices. However, these user interfaces typically center on the visual modality as the primary output channel, with audio output serving a secondary role. This paper argues for an increased need for shared input multimodal user interfaces for mobile devices. A shared input multimodal interface can be operated independently using a specific output modality, leaving users to choose the preferred method of interaction in different scenarios. We evaluate the value of a shared input multimodal menu system in both a single-task desktop setting and in a dynamic dual-task setting, in which the user was required to interact with the shared input multimodal menu system while driving a simulated vehicle. Results indicate that users were faster at locating a target item in the menu when visual feedback was provided in the single-task desktop setting, but in the dual-task driving setting, visual output presented a significant source of visual distraction that interfered with driving performance. In contrast, auditory output mitigated some of the risk associated with menu selection while driving. A shared input multimodal interface allows users to take advantage of multiple feedback modalities properly, providing a better overall experience

Boundary integrated neural networks (BINNs) for acoustic radiation and scattering

This paper presents a novel approach called the boundary integrated neural networks (BINNs) for analyzing acoustic radiation and scattering. The method introduces fundamental solutions of the time-harmonic wave equation to encode the boundary integral equations (BIEs) within the neural networks, replacing the conventional use of the governing equation in physics-informed neural networks (PINNs). This approach offers several advantages. Firstly, the input data for the neural networks in the BINNs only require the coordinates of "boundary" collocation points, making it highly suitable for analyzing acoustic fields in unbounded domains. Secondly, the loss function of the BINNs is not a composite form, and has a fast convergence. Thirdly, the BINNs achieve comparable precision to the PINNs using fewer collocation points and hidden layers/neurons. Finally, the semi-analytic characteristic of the BIEs contributes to the higher precision of the BINNs. Numerical examples are presented to demonstrate the performance of the proposed method

The Arabidopsis NLP7 gene regulates nitrate signaling via NRT1.1-dependent pathway in the presence of ammonium.

Nitrate is not only an important nutrient but also a signaling molecule for plants. A few of key molecular components involved in primary nitrate responses have been identified mainly by forward and reverse genetics as well as systems biology, however, many underlining mechanisms of nitrate regulation remain unclear. In this study, we show that the expression of NRT1.1, which encodes a nitrate sensor and transporter (also known as CHL1 and NPF6.3), is modulated by NIN-like protein 7 (NLP7). Genetic and molecular analyses indicate that NLP7 works upstream of NRT1.1 in nitrate regulation when NH4+ is present, while in absence of NH4+, it functions in nitrate signaling independently of NRT1.1. Ectopic expression of NRT1.1 in nlp7 resulted in partial or complete restoration of nitrate signaling (expression from nitrate-regulated promoter NRP), nitrate content and nitrate reductase activity in the transgenic lines. Transcriptome analysis revealed that four nitrogen-related clusters including amino acid synthesis-related genes and members of NRT1/PTR family were modulated by both NLP7 and NRT1.1. In addition, ChIP and EMSA assays results indicated that NLP7 may bind to specific regions of the NRT1.1 promoter. Thus, NLP7 acts as an important factor in nitrate signaling via regulating NRT1.1 under NH4+ conditions

Boundary integrated neural networks (BINNs) for 2D elastostatic and piezoelectric problems: Theory and MATLAB code

In this paper, we make the first attempt to apply the boundary integrated neural networks (BINNs) for the numerical solution of two-dimensional (2D) elastostatic and piezoelectric problems. BINNs combine artificial neural networks with the well-established boundary integral equations (BIEs) to effectively solve partial differential equations (PDEs). The BIEs are utilized to map all the unknowns onto the boundary, after which these unknowns are approximated using artificial neural networks and resolved via a training process. In contrast to traditional neural network-based methods, the current BINNs offer several distinct advantages. First, by embedding BIEs into the learning procedure, BINNs only need to discretize the boundary of the solution domain, which can lead to a faster and more stable learning process (only the boundary conditions need to be fitted during the training). Second, the differential operator with respect to the PDEs is substituted by an integral operator, which effectively eliminates the need for additional differentiation of the neural networks (high-order derivatives of neural networks may lead to instability in learning). Third, the loss function of the BINNs only contains the residuals of the BIEs, as all the boundary conditions have been inherently incorporated within the formulation. Therefore, there is no necessity for employing any weighing functions, which are commonly used in traditional methods to balance the gradients among different objective functions. Moreover, BINNs possess the ability to tackle PDEs in unbounded domains since the integral representation remains valid for both bounded and unbounded domains. Extensive numerical experiments show that BINNs are much easier to train and usually give more accurate learning solutions as compared to traditional neural network-based methods